This post was written by Justin Smith, Elle Buser, Emma Hart, and Ben Hueneman and published with minor edits. The team was advised by Dr. Lars Ruthotto. In addition to this post, the team has also created slides for a midterm presentation, a poster blitz video, code, and a paper.
During our summer research at Emory University 2021 REU/RET program, our group focused on the algorithmic diagnosis of Chiari malformation from DENSE MRIs. We created an algorithm that can accurately and efficiently segment the cerebellum and brain stem from a magnitude image and use displacement data to classify whether or not a patient has the Chiari malformation. In doing so, we investigated two approaches; one that segments the given image by aligning and comparing the image to a known atlas and another that segments through deep learning.
Chiari malformation is a condition in which brain tissue extends into the spinal canal. While it can be difficult to diagnose Chari from anatomical images, a promising new direction for diagnosis is by looking at brain movement . Using an MRI technique called DENSE (shown below) that records how the brain moves, Dr. Oshinsky’s group (at Emory’s Dept. of Radiology) collected data about how Chiari patients have more brain movement in the cerebellum and brainstem than controls.
This method may be more accurate in diagnosing Chiari, however, the large number of manual processing steps may limit its use as a wide-spread screening tool. This project aims at exploring the use of machine learning algorithms to automize parts of the image processing pipeline, most critically the segmentation of the image into different brain regions. The teams worked with image data that has been collected and labeled by Dr. Oshinski’s group in a previous research study. The project is accessible to the team members since we can build upon recent progress and software made in image processing and computer vision and the image data is two-dimensional and of limited resolution, which enables fast experimentation. Despite this simplicity, the project allows us to investigate ML in a realistic setting and investigate the generalization properties and robustness of the approach.
We develop this project to solve the problem of identifying where the
brain stem and cerebellum are in a given MRI. By finding or, in the
language of the field, by segmenting the brain stem and cerebellum, we
find the most relevant regions to look at brain movement. Using the
DENSE MRI data, we can then average the movement over those regions to
produce a biomarker that can help predict whether or not a patient has
the Chiari Malformation. By producing these segmentations (examples
above) automatically with the machine learning or atlas-based
approaches, the diagnosis process could become much cheaper and more
efficient.
We first looked into atlas-based image registration as a way to produce automatic segmentations of the brain stem and cerebellum. Using the FAIR toolbox in MATLAB, the idea behind this method was to have a bank of MRI images with manually drawn segmentations that we could compare a new MR image to. Once we find a transformation (example below) between the known and new images, we can use the same transformation to produce a new segmentation from the know one.